FedRMS: Privacy-Preserving Federated Knowledge Graph Embedding Through Randomization
Bibliographic record
Abstract
Recent years have witnessed a growing interest in Federated Knowledge Graph Embedding, driven by its potential to leverage knowledge from various data owners to improve link prediction performance without the need for data sharing. Existing works typically assume that the central Federated Learning (FL) server owns a table containing unique entities/relations for all FL clients. In addition, all clients are assumed to use the same knowledge graph embedding method. However, these methods are vulnerable to privacy leakage and do not fully explore the different contributions of local entity embeddings. To bridge this gap, we propose a randomized embedding method selection approach for privacy-preserving federated knowledge graph embedding (FedRMS). It selects a knowledge graph embedding method for each client during the local training process with randomness and employs an attention-based aggregator to derive the global entity embedding on the FL server. Extensive experiments on three real-world public datasets demonstrate that FedRMS achieves significant improvements in terms of both privacy preservation and link prediction against 5 state-of-the-art methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".